Add the test for tacotron2 loss to CI

Author: yangarbiter

examples/pipeline_tacotron2/README.md

@@ -1,18 +1 @@
 This is an example pipeline for text-to-speech using Tacotron2.
-
-
-## Instructions for running tests
-
-#### Install required the package for testing
-
-```bash
-pip install pytest
-```
-
-#### Run tests
-
-Execute the following command in the directory `examples/pipeline_tacotron2/`
-
-```bash
-pytest .
-```

examples/pipeline_tacotron2/loss/loss_function.py renamed to examples/pipeline_tacotron2/loss.py

@@ -31,7 +31,7 @@
 
 
 class Tacotron2Loss(nn.Module):
-    """Tacotron2 loss function adapted from:
+    """Tacotron2 loss function modified from:
     https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/loss_function.py
     """
 

examples/pipeline_tacotron2/loss/__init__.py

@@ -1,23 +1,23 @@
 import torch
-import unittest
 
 from .tacotron2_loss_impl import (
     Tacotron2LossShapeTests,
     Tacotron2LossTorchscriptTests,
     Tacotron2LossGradcheckTests,
 )
+from torchaudio_unittest.common_utils import PytorchTestCase
 
 
-class TestTacotron2LossShapeFloat32CPU(unittest.TestCase, Tacotron2LossShapeTests):
+class TestTacotron2LossShapeFloat32CPU(PytorchTestCase, Tacotron2LossShapeTests):
     dtype = torch.float32
     device = torch.device("cpu")
 
 
-class TestTacotron2TorchsciptFloat32CPU(unittest.TestCase, Tacotron2LossTorchscriptTests):
+class TestTacotron2TorchsciptFloat32CPU(PytorchTestCase, Tacotron2LossTorchscriptTests):
     dtype = torch.float32
     device = torch.device("cpu")
 
 
-class TestTacotron2GradcheckFloat64CPU(unittest.TestCase, Tacotron2LossGradcheckTests):
+class TestTacotron2GradcheckFloat64CPU(PytorchTestCase, Tacotron2LossGradcheckTests):
     dtype = torch.float64   # gradcheck needs a higher numerical accuracy
     device = torch.device("cpu")

examples/pipeline_tacotron2/loss/tacotron2_loss_gpu_test.py

@@ -0,0 +1,26 @@
+import torch
+
+from .tacotron2_loss_impl import (
+    Tacotron2LossShapeTests,
+    Tacotron2LossTorchscriptTests,
+    Tacotron2LossGradcheckTests,
+)
+from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
+
+
+@skipIfNoCuda
+class TestTacotron2LossShapeFloat32CUDA(PytorchTestCase, Tacotron2LossShapeTests):
+    dtype = torch.float32
+    device = torch.device("cuda")
+
+
+@skipIfNoCuda
+class TestTacotron2TorchsciptFloat32CUDA(PytorchTestCase, Tacotron2LossTorchscriptTests):
+    dtype = torch.float32
+    device = torch.device("cuda")
+
+
+@skipIfNoCuda
+class TestTacotron2GradcheckFloat64CUDA(PytorchTestCase, Tacotron2LossGradcheckTests):
+    dtype = torch.float64   # gradcheck needs a higher numerical accuracy
+    device = torch.device("cuda")

test/torchaudio_unittest/example/tacotron2/__init__.py

@@ -1,41 +1,8 @@
-import os
-import unittest
-import tempfile
-
 import torch
 from torch.autograd import gradcheck, gradgradcheck
 
-from .loss_function import Tacotron2Loss
-
-
-class TempDirMixin:
-    """Mixin to provide easy access to temp dir"""
-
-    temp_dir_ = None
-
-    @classmethod
-    def get_base_temp_dir(cls):
-        # If TORCHAUDIO_TEST_TEMP_DIR is set, use it instead of temporary directory.
-        # this is handy for debugging.
-        key = "TORCHAUDIO_TEST_TEMP_DIR"
-        if key in os.environ:
-            return os.environ[key]
-        if cls.temp_dir_ is None:
-            cls.temp_dir_ = tempfile.TemporaryDirectory()
-        return cls.temp_dir_.name
-
-    @classmethod
-    def tearDownClass(cls):
-        super().tearDownClass()
-        if cls.temp_dir_ is not None:
-            cls.temp_dir_.cleanup()
-            cls.temp_dir_ = None
-
-    def get_temp_path(self, *paths):
-        temp_dir = os.path.join(self.get_base_temp_dir(), self.id())
-        path = os.path.join(temp_dir, *paths)
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-        return path
+from pipeline_tacotron2.loss import Tacotron2Loss
+from torchaudio_unittest.common_utils import TempDirMixin
 
 
 class Tacotron2LossInputMixin(TempDirMixin):